SOTAVerified

Active Learning

Active Learning is a paradigm in supervised machine learning which uses fewer training examples to achieve better optimization by iteratively training a predictor, and using the predictor in each iteration to choose the training examples which will increase its chances of finding better configurations and at the same time improving the accuracy of the prediction model

Source: Polystore++: Accelerated Polystore System for Heterogeneous Workloads

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 761–770 of 3073 papers

TitleStatusHype
APLenty: annotation tool for creating high-quality datasets using active and proactive learning—0
A novel machine learning-based optimization algorithm (ActivO) for accelerating simulation-driven engine design—0
Applied Federated Model Personalisation in the Industrial Domain: A Comparative Study—0
Applied metamodelling for ATM performance simulations—0
Applying LLMs to Active Learning: Towards Cost-Efficient Cross-Task Text Classification without Manually Labeled Data—0
Active Learning of Ordinal Embeddings: A User Study on Football Data—0
ALEVS: Active Learning by Statistical Leverage Sampling—0
A Practical & Unified Notation for Information-Theoretic Quantities in ML—0
A Pre-trained Data Deduplication Model based on Active Learning—0
Active Learning for Vision-Language Models—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1TypiClustAccuracy93.2—Unverified
2PT4ALAccuracy93.1—Unverified
3Learning lossAccuracy91.01—Unverified
4CoreGCNAccuracy90.7—Unverified
5Core-setAccuracy89.92—Unverified
6Random Baseline (Resnet18)Accuracy88.45—Unverified
7Random Baseline (VGG16)Accuracy85.09—Unverified